Top Machine Learning Jobs in Krakow

Lead GitLab’s company-wide strategy for autonomous, agentic software development workflows. Identify and validate AI opportunities across the SDLC, build prototypes and reference architectures, establish evaluation frameworks and safety controls, and drive internal adoption. Partner across Engineering, Product, Infrastructure, Security, Architecture, and Data and ML teams to scale reliable, observable, compliant agentic capabilities into customer-facing products. Mentor senior technical leaders and represent GitLab externally on AI-assisted development and productivity measurement.

Leads analytical methodology and evaluation for AI-based healthcare and pharmaceutical workflows. Owns model selection, cohort and HCP segmentation, agent decision logic, and evaluation frameworks. Partners with engineering, data architecture, and governance teams to productionize rigorous analytical methods, identify new data and techniques, communicate insights, and mentor data science and engineering colleagues.

Leads data science methodology and advanced analytics for AI-enabled Medical Affairs workflows. Develops and evaluates statistical, machine-learning, NLP, and AI models using healthcare and real-world data; defines validation, monitoring, and decision frameworks; partners with engineering, governance, and business teams to operationalize solutions. Provides technical leadership, mentorship, documentation standards, and strategic guidance while ensuring privacy, compliance, responsible AI use, and clear communication of analytical findings.

Leads ad hoc and exploratory analyses for medical and business questions across a global Medical Insights organization. Prepares and validates datasets, translates ambiguous stakeholder needs into decision-ready findings, and maintains analytical documentation. Tests new data sources, methods, and AI-enabled approaches through proof-of-concept analyses to guide platform investments and roadmap decisions. Partners with business and product stakeholders while supporting compliant, reproducible use of regulated data.

Develop OAM management solutions for 5G/6G RAN, design and implement rApps on Non-RT EIC, integrate AI/ML features, drive studies, bridge design and verification, own technical areas, engage in standardization and customer interactions.

Design, build, deploy, and evaluate production-grade generative AI applications using foundation models and LLMs. Develop RAG systems, AI agents, fine-tuned models, evaluation pipelines, and high-quality datasets for financial workflows. Optimize latency and cost while implementing scalable deployment, monitoring, safety guardrails, and responsible AI practices. Collaborate cross-functionally to deliver AI-powered solutions for finance and accounting teams.

Design and specify RAN Baseband system features for 5G, perform system studies in radio resource management and physical layer algorithms, develop network-level simulators, analyze test and network performance, support product verification and deployment, contribute to standards and innovation, and collaborate globally across development and customer teams.

Design and develop scalable, high-quality backend systems for traffic automated road closure detection, handling the full software development lifecycle.

Designs, builds, and operates production-grade agentic AI systems and orchestration frameworks. Responsibilities include prompt architecture, tool and API integrations, monitoring, evaluation, cost controls, reliability improvements, and governance documentation. The engineer collaborates with data science, data engineering, and governance teams to ensure reliable, compliant workflows using structured healthcare and pharmaceutical data.

AI/ML Engineers will build and deploy AI-native products, autonomous agents, LLM applications, RAG systems, and production ML pipelines. Responsibilities may include integrating AI into crypto infrastructure and consumer products, fine-tuning and evaluating models, optimizing inference, and developing agentic workflows and machine-to-machine payment systems. Engineers will work end-to-end with high-ownership teams in fast-moving environments.

Drive business development and close high-value enterprise deals with global biotech and biopharma accounts across Europe. Own strategic customer relationships, deliver recurring revenue, partner with technical and customer success teams on complex proposals, provide market insights for product direction, build industry credibility, and improve enterprise sales processes and playbooks.

Conduct advanced research in artificial intelligence and machine learning, including deep learning, graphical models, reinforcement learning, computer perception, natural language processing, and data representation. Define complex business problems, design and deliver modeling solutions, evaluate model performance, establish scientific and engineering specifications, and build analytical roadmaps. Assess production implementation requirements, improve data capture methods, apply statistical and mathematical techniques, and resolve root causes and process bottlenecks.

Lead product vision and discovery for a genomics-driven diagnostic and personalization platform. Validate hypotheses with patients, clinicians, and partners; translate research into product specs; coordinate labs and data providers; partner with engineers and researchers to build AI prototypes; shape data, regulatory, and GTM strategy while making core product and architectural decisions.

Lead delivery of real-time fraud decisioning services and platform capabilities for international markets. Design and implement backend services and APIs, integrate regional vendors and ML/AI signals, support operations and on-call, and mentor engineers to maintain high-quality, scalable systems.

The AI Engineer will build data pipelines and improve ML systems for reliability in production. Responsibilities include schema design, data infrastructure, and model evaluation, ensuring high-quality outputs and integration with product workflows.

Provides strategic and hands-on biostatistical leadership across pharmaceutical discovery, translational, biomarker, and early research programs. Designs studies, analyzes complex biological datasets, develops advanced statistical methods and reproducible R workflows, and translates evidence into portfolio decisions. The role collaborates with quantitative and scientific functions, supports publications and external partnerships, mentors statisticians, and advances data-driven analytical practices.

Design, develop, and maintain high-performance, secure Java backend services and APIs. Ensure reliability, scalability, and cost-efficiency across microservices, databases, messaging systems, and capacity planning. Troubleshoot, profile, benchmark, and optimize systems (including ML applications) and collaborate with Kazakhstan stakeholders.

Build and maintain production-grade native iOS applications using Swift and SwiftUI, integrating AI features through backend APIs. Design resilient real-time user experiences, optimize performance and resource usage, collect telemetry, and collaborate with ML and backend teams. The role requires shipping consumer-facing App Store applications with reliable networking, graceful degradation, concurrency, and on-device machine learning capabilities.

Own end-to-end talent acquisition and headhunting for Arago’s technical hiring across France, North America, and Israel. Partner with the CEO, CTOs, and engineering leaders to define roles, source and close senior engineers, scientists, and technical leaders across hardware, optical, software, and research. Build proactive sourcing pipelines, improve interview processes and hiring standards, monitor pipeline health and hiring velocity, and communicate progress to leadership.

Analyze large datasets and apply statistical and ML techniques to measure and improve platform performance and reliability. Build diagnostic tools, define KPIs, mine internal data feeds, automate analytical workflows, and partner with engineering, operations, and product teams to influence technical decisions and increase system reliability.

Build and maintain production-grade ML platform infrastructure, including training orchestration, model lifecycle automation, multi-tenant environments, deployment workflows, observability, drift monitoring, reproducibility, cost tracking, and CI/CD automation. Collaborate with DevOps and SRE teams while documenting operational processes and supporting platform handover.

Own an AI investing product vertical end-to-end, including strategy, metrics, roadmap, technical design, delivery, and launch. Partner with engineering on APIs, data flows, ledgering, reliability, security, and scalability. Incorporate KYC, transaction monitoring, limits, privacy, licensing, and regulatory requirements. Apply AI with evaluation, safeguards, human oversight, and failure handling while coordinating Engineering, Design, Compliance, Operations, Treasury, and Finance.

Build and maintain Ergodic AI’s AI platform across back-end and front-end systems. The role involves developing with Python web frameworks and JavaScript/TypeScript, integrating with client technology stacks and workflows, applying AI, ML, and LLM concepts, and contributing to DevOps, cloud deployment, testing, and infrastructure practices. As an early-stage hire, the engineer will help shape the product and solve complex data and AI challenges.

Own an AI stockbroking product vertical end-to-end, including strategy, metrics, roadmap, technical design, delivery, and launch. Partner with engineering on APIs, data flows, ledgering, reconciliation, reliability, security, and scalability. Embed KYC, transaction monitoring, limits, privacy, and licensing requirements. Apply AI with evaluation, safeguards, human oversight, and failure handling while coordinating Engineering, Design, Compliance, Operations, Treasury, and Finance.

Senior technical leader providing high-level AI strategy, architecture direction, and long-term systems design. Delivers strategic artifacts (architecture reviews, AI integration strategies, roadmaps), advises executives, collaborates with product and engineering teams, and mentors technical leaders to improve scalability, resilience, and governance across platforms.